Abstract

Satellite image analysis often encounters challenging foreground-background separation due to complex textures and illumination variations. We introduce an unsupervised ensemble semantic segmentation framework that aggregates complementary clustering models to effectively delineate boundaries without requiring manual annotations. Comprehensive experiments show that our ensemble method outperforms baseline clustering models in separation accuracy and edge preservation.